Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SEIT: Structural Enhancement for Unsupervised Image Translation in Frequency Domain
Authors: Zhifeng Zhu, Yaochen Li, Yifan Li, Jinhuo Yang, Peijun Chen, Yuehu Liu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experimental results well demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1School of Software Engineering, Xi an Jiaotong University 2Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | The datasets we use include SYNTHIA(Ros et al. 2016), GTA5(Richter et al. 2016), Cityscapes(Cordts et al. 2015) and BDD(Yu et al. 2020). |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits, percentages, or explicit methodology for data partitioning. |
| Hardware Specification | Yes | All experiments are conducted on a single RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'VGG network' but does not provide specific version numbers for any programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | The batch size is set to 1. We use the Adam optimizer with β1 = 0.5 and β2 = 0.999. The initial learning rate is set to 0.0002 and the step decay learning strategy is used, with the learning rate decaying to half of the original learning rate every 5 epochs. The model is trained for 100 epochs. Following previous work, the loss weight in equation 14 is set to 1.0, 2.0, and 1.0, respectively. |